YIQ Color Space Research Articles

Objective color image quality assessment based on Sobel magnitude

Image quality assessment algorithms aim to evaluate the perceptual quality of an image by assigning an evaluation score. By comparing the scores, the perceptual similarity or difference between two images can be assessed. In this paper, we present a new full-reference image quality metric method which was developed by combining Sobel magnitude and chrominance information in the YIQ color space. But differing from existing methods, our model incorporates color intensity adaptation to extract and enhance perceptually significant image features. The proposed metrics are tested on three well-known databases available in the literature (TID2013, TID2008, and CSIQ). Experimental results presented to confirm that the proposed metric is an effective and have low computational complexity.

Locally estimated heterogeneity property and its fuzzy filter application for deinterlacing

This paper presents an intra-field scanning format conversion method using two filters: bilinear filter (BF) and fuzzy-based weighted average filter (FWAF). The proposed method is intended for black and white images, luminance component of YIQ color space, or each color component of RGB color space. We start from the notion that pixels to be interpolated can be classified into two areas based on local variance: homogeneous and heterogeneous areas. According to the local variance criteria, we apply the FWAF to the heterogeneous area and the BF to the homogeneous one, producing good visual results. Our FWAF consists of an intensity similarity filter and a geometric closeness filter. The latter is used to populate the heterogeneous area with the missing lines, due to its high deinterlacing precision. Our experimental results show that the proposed approach provides satisfactory performances in terms of both objective metrics and visual image quality. We used parameter tuning on our training set to explore the relationship between objective quality and computational complexity. We report on how to achieve good performance or the best quality-speed tradeoff using the methods researched.

A novel local mesh color texture pattern for image retrieval system

This manuscript contributes a new feature descriptor local mesh color texture pattern (LMCTP) by merging color and local spatial information for image retrieval system. The intended method combines the various features from a mixture of color spaces, the local mesh patterns of the input image. LMCTP descriptor discriminates information between the spatial color textural patterns of multi-spectral channels within the local region. It determines the relationship between surrounding neighbors of the opponent color channel. Beside this, a merged color space, I1QCb, is constructed by combining the I1 part of the I1I2I3 color space and the chromatic component images, Cb and Q, of the YCbCr and YIQ color spaces correspondingly so as to exploit the effect of joint color texture feature. The I1QCb color space, whose component of images having matching characteristics, increases the distinctive power for retrieval system. Then opponent color spaces I1Q, QCb and I1Cb are utilized for the capturing of LMCTP features. The feasibility of the proposed framework is experiments on benchmark databases such as MIT VisTex database (DB1), Corel 1000 database (DB2). Experimental study shows that the proposed pattern gives average retrieval precision of 99.8 for DB1 and 76.5 for DB2, respectively.

No-reference image sharpness assessment in autoregressive parameter space.

In this paper, we propose a new no-reference (NR)/blind sharpness metric in the autoregressive (AR) parameter space. Our model is established via the analysis of AR model parameters, first calculating the energy- and contrast-differences in the locally estimated AR coefficients in a pointwise way, and then quantifying the image sharpness with percentile pooling to predict the overall score. In addition to the luminance domain, we further consider the inevitable effect of color information on visual perception to sharpness and thereby extend the above model to the widely used YIQ color space. Validation of our technique is conducted on the subsets with blurring artifacts from four large-scale image databases (LIVE, TID2008, CSIQ, and TID2013). Experimental results confirm the superiority and efficiency of our method over existing NR algorithms, the stateof-the-art blind sharpness/blurriness estimators, and classical full-reference quality evaluators. Furthermore, the proposed metric can be also extended to stereoscopic images based on binocular rivalry, and attains remarkably high performance on LIVE3D-I and LIVE3D-II databases.

Segmentation of acne lesion using fuzzy C-means technique with intelligent selection of the desired cluster.

Segmentation is the basic and important step for digital image analysis and understanding. Segmentation of acne lesions in the visual spectrum of light is very challenging due to factors such as varying skin tones due to ethnicity, camera calibration and the lighting conditions. In this approach the color image is transformed into various color spaces. The image is decomposed into the specified number of homogeneous regions based on the similarity of color using fuzzy C-means clustering technique. Features are extracted for each cluster and average values of these features are calculated. A new objective function is defined that selects the cluster holding the lesion pixels based on the average value of cluster features. In this study segmentation results are generated in four color spaces (RGB, rgb, YIQ, I1I2I3) and two individual color components (I3, Q). The number of clusters is varied from 2 to 6. The experiment was carried out on fifty images of acne patients. The performance of the proposed technique is measured in terms of the three mostly used metrics; sensitivity, specificity, and accuracy. Best results were obtained for Q and I3 color components of YIQ and I1I2I3 color spaces with the number of clusters equal to three. These color components show robustness against non-uniform illumination and maximize the gap between the lesion and skin color.

Automated calculation of ptosis on lateral clinical photographs.

The goal is to fully automate the calculation of a breast ptosis measure from clinical photographs through automatic localization of fiducial points relevant to the measure. Sixty-eight women (97 clinical photographs) who underwent or were scheduled for breast reconstruction were included. The photographs were divided into a development set (N = 49) and an evaluation set (N = 48). The breast ptosis measure is obtained automatically from distances between three fiducial points: the nipple, the lowest visible point of breast (LVP), and the lateral terminus of the inframammary fold (LT). The nipple is localized using the YIQ colour space to highlight the contrast between the areola and the surrounding breast skin. The areola is localized using its shape, location and high Q component intensity. The breast contour is estimated using Dijkstra's shortest path algorithm on the gradient of the photograph in greyscale. The lowest point of the estimated contour is set as the LVP. To locate the anatomically subtle LT, the location of patient's axilla is used as a reference. The algorithm's efficacy was evaluated by comparing manual and automated localizations of the fiducial points. The average nipple diameter was used as a cut-off to define success. The algorithm showed 90, 91 and 83% accuracy for locating the nipple, LVP and LT in the evaluation set, respectively. This study presents a new automated algorithm that may facilitate the quantification of breast ptosis from lateral views of patients' photographs.

Hybrid Human Skin Detection Using Neural Network and K-Means Clustering Technique

Human skin detection is an essential step in most human detection applications, such as face detection. The performance of any skin detection system depends on assessment of two components: feature extraction and detection method. Skin color is a robust cue used for human skin detection. However, the performance of color-based detection methods is constrained by the overlapping color spaces of skin and non-skin pixels. To increase the accuracy of skin detection, texture features can be exploited as additional cues. In this paper, we propose a hybrid skin detection method based on YIQ color space and the statistical features of skin. A Multilayer Perceptron artificial neural network, which is a universal classifier, is combined with the k-means clustering method to accurately detect skin. The experimental results show that the proposed method can achieve high accuracy with an F1-measure of 87.82% based on images from the ECU database.

Automatic Method for Contrast Enhancement of Natural Color Images

The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms.

Enhanced Machine Vision System for Ripe Fruit Detection Based on Robotic Harvesting

The proposed study intends to provide an efficient algorithm for the instruction of an automatic robot arm to choose the ripe fruits on the tree. Steps involved in this study are recognizing and locating the ripe fruits from the leaf and branch portions by using an efficient machine vision algorithm. Initially, discrete wavelet transform is used for better preserving of edges and fine details in the given input image. Then RGB, HSV, L*a*b* and YIQ color spaces were studied to segment the ripe fruits from the surrounding objects. Finally, the results showed that 'I' component of the YIQ color space has the best criterion for recognizing the fruit from the foliage. The fruit segmentation based on machine vision has an occlusion problem. In this proposed method these problems are also examined.

DWT-Based Watermarking Using 2DLDA for Color Images

In this paper, we propose a watermarking algorithm based on Discrete Wavelet Transform (DWT) by using Two-dimensional Linear Discriminant Analysis (2DLDA) for color images. At first, the color image is converted into the YIQ color space and then transformed into the frequency domain by DWT. During the embedding stage, two watermarks of reference and logo are embedded into the quadrature chrominance component. Then, watermark extraction is done by 2DLDA from the quadrature chrominance component based on DWT. By embedding the reference watermark and using 2DLDA, the training covariance matrix can be directly computed from 2D image samples and the robustness of watermark extraction can be ensured. Also, the computation complexity can be reduced. By considering the Human Visual System (HVS), experimental results have shown that the watermark can be correctly extracted and better robustness is provided after various image attacks.

Digital Image Watermarking using Fibonacci Transform in YIQ Color Space

Digital watermarking is a technique for inserting ownership information to the digital data to prove the authenticity. A digital watermarking is perceptually invisible to prevent obstruction of the original image. It is also used for tamper proofing, broadcast monitoring, covert communication etc. In this paper, a wavelet-based watermarking approach for hiding watermark image in color host images is proposed. The host image is converted into YIQ color space and then Y channel decomposed into wavelet coefficients. This proposed methodology overcomes the drawback of the existing approaches and enhances the security by applying Arnold transform and Fibonacci transform for embedding the resultant pattern in the host image. The watermark embedding process does not degrade the visual quality of the image.

Color Image Enhancement on YIQ Color Space

The three components of the color images enhance the images directly in the RGB color space, will cause distortion of the image. So our paper will convert true color image from RGB space to YIQ space, the holomorphic filtering and histogram equalization are executed on Y component to make the image enhancement, the Y component contains a lot of image information; finally, the image is converted from YIQ color space to RGB color space once again. Experimental results show that the approach which were proposed in the paper, Combined with the method of holomorphic filtering and histogram equalization to overcome the uneven illumination, the image is dark and other shortcomings to achieve satisfactory enhancement.

A Method to Enhance the Fog Image Based on Dark Object Subtraction

Aiming at the problem of poor image contrast and low visibility, a single image contrast enhancement method is put forward in this paper. The method is based on Dark-object subtraction technique, translating the fog degraded image from RGB color space to YIQ color space, and taking out the Y component. Then using the maximum entropy method to get the threshold value of image segmentation, we can put different portion of the image according to the different formula for image restoration. The processed image must be converted from YIQ color space to RGB color space In the back of the steps. Finally, the image needs a linear dynamic range adjustment to enhance the contrast and brightness. Experiments show that the method can effectively remove haze effect on the image. The dehazing effect of the processed image is obvious. The image becomes clear and bright, and the details is outstanding, which is convenient for observation and analysis.

Mean shift segmentation on Enhanced Color image

This paper proposed the method of mean shift segmentation on Enhanced color image . The method applies the modified histogram equalization technique for enhancement of under illuminated color image and then mean shift segmentation is applied on this enhanced image. This technique uses the lightness component in YIQ color space is transformed using sigmoid function, then the traditional histogram equalization (HE) method is applied on Y component. Once the image is enhanced mean shift segmentation is applied to the enhanced image due to which we get good segmentation results as compared to without enhanced image. Experiments on natural color images show promising results.

An Adaptive Blind Watermarking Algorithm for Color Image

This paper proposed a novel blind color digital watermarking algorithm based on Integer Discrete Wavelet Transform (IDWT) and Human Visual System (HVS). Firstly, color watermark image was processed into one dimension digital information, and color host image was converted into YIQ color space. Then, according to the features of HVS, encrypted watermark was embedded adaptively into the Y luminance component of the YIQ color space in IDWT domain. The proposed algorithm allowed extracting watermark without the help of original watermark and host image. Experiment results show that the embedded watermark is invisible and robust against common image attacks. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1900

Alternative Diffuse Lighting and Specular Reflection Approach Using YIQ Color Space for 3D Scene Visualization Using Programmable HLSL Shaders

In this work an alternative approach for diffuse lighting and specular reflections is presented that uses YIQ color spaceinstead of traditional RGB color space. The classical illumination algorithms that rely on RGB color space may lead tounrealistic results either due to the fact that they cannot make the original color brighter (inherent limitation imposedby using the RGB color space) or produce incorrect shades when viewed from certain angles because in thecalculation of the specular reflection the original texture color is not taken into account.The approach proposed in this work gets around the problem by applying illumination in YIQ color space using its“luma” component (Y). In this novel approach the diffuse lighting is capable of increasing the perceived brightness ofthe source texture and the resulting color is always dependant on the surface’s texture and produces reasonablyrealistic results when viewed from any possible angle. The algorithm and its HLSL shader code are described in thiswork along with the experiments that illustrate the problem and the solution. The performance benchmarks are alsoprovided, showing that the proposed approach is a viable and realistic solution for applications running in real-time.

Unsupervised building detection in complex urban environments from multispectral satellite imagery

A generic algorithm is presented for automatic extraction of buildings and roads from complex urban environments in high-resolution satellite images where the extraction of both object types at the same time enhances the performance. The proposed approach exploits spectral properties in conjunction with spatial properties, both of which actually provide complementary information to each other. First, a high-resolution pansharpened colour image is obtained by merging the high-resolution panchromatic (PAN) and the low-resolution multispectral images yielding a colour image at the resolution of the PAN band. Natural and man-made regions are classified and segmented by the Normalized Difference Vegetation Index (NDVI). Shadow regions are detected by the chromaticity to intensity ratio in the YIQ colour space. After the classification of the vegetation and the shadow areas, the rest of the image consists of man-made areas only. The man-made areas are partitioned by mean shift segmentation where some resulting segments are irrelevant to buildings in terms of shape. These artefacts are eliminated in two steps: First, each segment is thinned using morphological operations and its length is compared to a threshold which is determined according to the empirical length of the buildings. As a result, long segments which most probably represent roads are masked out. Second, the erroneous thin artefacts which are classified by principal component analysis (PCA) are removed. In parallel to PCA, small artefacts are wiped out based on morphological processes as well. The resultant man-made mask image is overlaid on the ground-truth image, where the buildings are previously labelled, for the accuracy assessment of the methodology. The method is applied to Quickbird images (2.4 m multispectral R, G, B, near-infrared (NIR) bands and 0.6 m PAN band) of eight different urban regions, each of which includes different properties of surface objects. The images are extending from simple to complex urban area. The simple image type includes a regular urban area with low density and regular building pattern. The complex image type involves almost all kinds of challenges such as small and large buildings, regions with bare soil, vegetation areas, shadows and so on. Although the performance of the algorithm slightly changes for various urban complexity levels, it performs well for all types of urban areas.

Comparative Performance Analysis of DWT-SVD BasedColor Image Watermarking Technique in YUV, RGB and YIQ Color Spaces

In this paper, robust color image watermarking technique based on discrete wavelet transform-singular value decomposition (DWT-SVD) with Arnold scrambling is proposed and tested with YUV, RGB and YIQ color spaces. The PSNR and NC are compared for YUV, RGB and YIQ color spaces and complete analysis is presented. The decomposition is done with 'Haar' which is simple, symmetric and orthogonal wavelet and the direct weighting factor is used in watermark embedding and extraction process. The results are tested with standard database color images of size 512ൈ512 and grey scale watermark of 64ൈ64 sizes. The presented scheme is non blind. The maximum recorded PSNR is 53.1701 for R channel and up to 52.3337 for Y channel and maximum value of NC equal to 0.9997 for U channel and average value remains 0.99 indicating best recovery of watermark for all channels. The proposed scheme is also tested for different attacks like compression, scaling, rotation, cropping noise etc in YUV, RGB and YIQ color spaces and comparative results are presented.

An Extended Performance Comparison of Colour to Grey and Back using the Haar, Walsh, and Kekre Wavelet Transforms

The storage of colour information in a greyscale image is not a new idea. Various techniques have been proposed using different colour spaces including the standard RGB colour space, the YUV colour space, and the YCbCr colour space. This paper extends the results described in [1] and [2]. While [1] describes the storage of colour information in a greyscale image using Haar wavelets, and [2] adds a comparison with Kekre’s wavelets, this paper adds a third transform – the Walsh transform and presents a detailed comparison of the performance of all three transforms across the LUV, YCbCr, YCgCb, YIQ, and YUV colour spaces. The main aim remains the same as that in [1] and [2], which is the storage of colour information in a greyscale image known as the “matted” greyscale image.

A Comparison of Haar Wavelets and Kekre’s Wavelets for Storing Colour Information in a Greyscale Image

The technique to store colour information within a greyscale image has been proposed previously using various colour spaces, such as the YCbCr, LUV, YCgCb, YUV, and YIQ colour spaces, and using the Haar wavelet transform. One such study is described in [1]. This research paper extends the study to another wavelet transform – Kekre‟s wavelet transform (abbreviated as KWT). The embedding of the colour information into the greyscale image leads to the formation of a slightly distorted greyscale image known as a “matted greyscale” image. This matted greyscale image is used to reconstruct the colour image.